Grizzly bears and forestry I. Selection of clearcuts by grizzly bears in
Bibliographic record
Abstract
We examined if clearcuts were selected as habitats by grizzly bears (Ursus arctos L.) in west-central Alberta during three seasons: hypophagia, early hyperphagia, and late hyperphagia. Our objectives were to describe seasonal habitat selection of clearcuts using resource selection functions at two scales. At the first scale, we assessed patch or third-order selection by comparing use (radiotelemetry) with study area-wide random locations and a dummy variable identifying whether locations occurred within or outside of clear-cut boundaries. At the second scale, we assessed within-patch or fourth-order selection by comparing locations (use and random) found within clearcuts and environmental covariates of terrain, silviculture, and landscape metrics. Finally, we examined diurnal versus crepuscular/nocturnal use of clearcuts by comparing the two with an expected 50:50 ratio. At the third-order scale, grizzly bears used clearcuts with respect to their availability for hypophagia and late hyperphagia, while selecting clearcuts more than expected during early hyperphagia. Fourth-order habitat selection revealed that landscape metrics, silviculture, and terrain were important predictors of grizzly bear use during hypophagia and late hyperphagia, while terrain appeared to be the most important predictor during early hyperphagia. Overall, grizzly bears avoided clear-cut interiors and preferred clearcuts with higher perimeter-to-edge ratios. Clearcuts were significantly more likely to be used during crepuscular/nocturnal periods. Intermediate-aged (30 years old) clearcuts were selected during hypophagia, whereas recent and old clearcuts were selected during late hyperphagia. Bears tended to avoid clearcuts with Donaren mound preparation, while
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".